Papers
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Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models
Atticus Geiger, Kyle Richardson, Christopher PottsBlackbox NLP • 2020In adversarial (challenge) testing, we pose hard generalization tasks in order to gain insights into the solutions found by our models. What properties must a system have in order to succeed at these hard tasks? In this paper, we argue that an essential factor is the ability to form modular… moreA Simple and Effective Model for Answering Multi-span Questions
Elad Segal, Avia Efrat, Mor Shoham, Amir Globerson, Jonathan BerantEMNLP • 2020Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for a model that generates text explicitly. However, forcing an answer to be a single span can be… moreA Simple Yet Strong Pipeline for HotpotQA
Dirk Groeneveld, Tushar Khot, Mausam, Ashish SabharwalEMNLP • 2020State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does their strong performance on popular… moreFact or Fiction: Verifying Scientific Claims
David Wadden, Kyle Lo, Lucy Lu Wang, Shanchuan Lin, Madeleine van Zuylen, Arman Cohan, Hannaneh HajishirziEMNLP • 2020We introduce the task of scientific fact-checking. Given a corpus of scientific articles and a claim about a scientific finding, a fact-checking model must identify abstracts that support or refute the claim. In addition, it must provide rationales for its predictions in the form of evidentiary… moreIs Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning
H. Trivedi, N. Balasubramanian, Tushar Khot, A. SabharwalEMNLP • 2020Has there been real progress in multi-hop question-answering? Models often exploit dataset artifacts to produce correct answers, without connecting information across multiple supporting facts. This limits our ability to measure true progress and defeats the purpose of building multihop QA datasets… moreLearning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering.
Harsh Jhamtani, P. ClarkEMNLP • 2020Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from. To address this, we introduce three explanation datasets in which explanations formed from corpus facts are… moreMore Bang for Your Buck: Natural Perturbation for Robust Question Answering
Tao Li, Daniel Khashabi, Tushar Khot, Ashish Sabharwal, V. SrikumarEMNLP • 2020Warning: This paper contains examples of stereotypes that are potentially offensive. While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and… moreQADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Valentina Pyatkin, Ayal Klein, Reut Tsarfaty, Ido DaganEMNLP • 2020Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding. However, annotating discourse relations typically requires expert annotators. Recently, different semantic aspects of a sentence have… moreSciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search
Tom Hope, Jason Portenoy, Kishore Vasan, Jonathan Borchardt, Eric Horvitz, Daniel S. Weld, Marti A. Hearst, Jevin D. WestEMNLP • Demo • 2020The COVID-19 pandemic has sparked unprecedented mobilization of scientists, already generating thousands of new papers that join a litany of previous biomedical work in related areas. This deluge of information makes it hard for researchers to keep track of their own field, let alone explore new… moreSLEDGE: A Simple Yet Effective Baseline for COVID-19 Scientific Knowledge Search
S. MacAvaney, Arman Cohan, N. GoharianEMNLP • 2020With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of literature on the virus. Clinicians, researchers, and policy-makers need a way to effectively search these articles. In this work, we present a search system… more